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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

static-subid: Deterministic subordinate UID/GID assignment for unprivileged containers

static-subid calculates predictable subordinate UID and GID ranges for Linux users based on their UID, ensuring consistent ID mappings across multiple systems. Unlike shadow-utils' sequential allocation (which depends on creation order), static-subid uses a deterministic formula that guarantees the same user UID always receives the same subordinate ID range. Subordinate IDs enable user namespaces for unprivileged container runtimes (Podman, Docker rootless mode, LXC) by mapping container UIDs/GIDs to host subordinate IDs without requiring root privileges.

Riehecky, Pat [Fermi National Accelerator Laborato↗
DOE OSTI2023

Impact of Thermal Scattering Law on Similarity Assessment in Light-Water or Polyethylene-Moderated Systems

A collaborative effort between Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) is underway to provide a technical basis and methodology for the criticality safety community to use the sum of fractions (SoF) method for generating limits for mixtures of “selected actinide nuclides” included in the ANSI/ANS 8.15 standard. The PNNL scope in this project is to define a range of mixtures of 233 U, 235 U, and 239 Pu moderated with either light water or polyethylene and to examine the critical masses for these mixtures. The ORNL scope is primarily to provide validation support for these studies. More complete discussion of the project and its validation aspects will be presented at the upcoming International Conference on Nuclear Criticality Safety (ICNC) this Fall in Sendai, Japan. Clear differences in benchmark similarity to application systems as assessed by the integral parameter ck are noted in the validation studies performed as part of this project as a function of moderator. The c k value is a correlation coefficient that represents that amount of shared uncertainty in k eff due to cross sections between two systems. Individual nuclide-reaction contributions between the two systems can be simply summed to arrive at the total c k value. Specifically, the c k values for light-water–moderated solution experiments are higher for a water-moderated application than for a polyethylene-moderated application. This result is neither totally unexpected nor surprising, but the magnitude of the difference was difficult to anticipate. The TSUNAMI sequence, in the SCALE 6.2.4 code package developed by ORNL, was used to generate eigenvalues and reactivity effects with perturbation-theory based approach through sensitivity coefficients for all nuclides in the system with all reactions and energy groups. The TSUNAMI-Indices and Parameters (IP) sequence then uses the sensitivity data generated through TSUNAMI to generate relational parameters (i.e., c k ) to determine the degree of similarity between systems. One detail of the SCALE material and data implementation must be discussed at this point. Several thermal scattering laws (TSLs) are available for 1 H. SCALE uses a different nuclide ID number for each TSL; essentially, each version of 1 H is treated as a unique nuclide. For example, 1 H bound in water ( 1 H-H2O) is assigned the nuclide ID 1001, whereas 1 H bound in polyethylene (h-poly) is assigned the nuclide ID 9001001. The same cross section data are used for all reactions in 1 H, regardless of TSL, except for scattering below the TSL cutoff energy. TSUNAMI-IP treats different nuclide IDs as different nuclides; thus, no uncertainty is shared between 1 H-H2O and h-poly, despite much of the same data, including covariance data, being used for both nuclides. This presents a question: how much of the difference in assessed similarity between water- and polyethylene-moderated systems is due to the moderators, and how much is caused by the treatment of 1 H-H2O and h poly with cross section and covariance data. The extended edits generated by TSUANMI-IP allow for an investigation of this issue specifically, as well as a demonstration of the general techniques available within TSUNAMI to understand the results of the similarity assessment. This paper presents and analyzes the similarity assessment of both water- and polyethylene-moderated systems for a single benchmark: PST-002-001.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor

Generation of quantum states of light through off-resonance four-wave mixing in rubidium vapor is a straightforward and well-established technique. However, achieving a sufficiently high and consistent level of intensity difference squeezing (IDS) and intrinsic quantum correlations between photon pairs over an extended timescale, necessary for quantum-light-based nonlinear optical spectroscopy and microscopy, remains challenging and largely unexplored. Here, we report a simple stabilization method combining an active periodic laser frequency retuning with an automatic control algorithm based on quantitatively assessing the factors affecting the IDS level and squeezed light intensity. Validation of our method was performed by acquiring data over a 5-hour period, and the results demonstrate a remarkably stable squeezing level of -7.8 dB in combination with a > 4× reduction of the standard deviation of IDS level from 0.46 dB to 0.10 dB. The achieved stabilization further enables us to quantitatively assess the IDS reduction due to the scattering in a polystyrene bead suspension as a function of sample transmission. Our approach should enable a variety of applications requiring an extended squeezing stability over multiple hours, especially for those following biological processes and chemical reactions in real time.

Allen, Christian Harry [Oak Ridge National Laborat↗
DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

SOC Synthetic Microstructure Bank

QUICK START: Start with property_library.html (can be found by typing the filename into the query box) and use the interactive table to filter, sort, and select a microstructure with the desired properties. Search for the alphabetic code to obtain the corresponding dataset. Full description: This is a bank of 1,970 unique 3-phase electrode microstructure files. When you account for reassigning phase IDs (e.g. declare that 1=Ni and 2=pore, instead of 1=pore and 2=Ni), it actually represents 5,910 unique electrode microstructures, each of which could be considered to be either an air or a fuel electrode (e.g. declare that the phase IDs correspond to pore, Ni, and YSZ; or that they correspond to pore, LSCF, and GDC; or whatever electron-conductor and ion-conductor combination is being studied). The voxel size is 50 nm and each electrode file contains a 4x4 grid of (12.5 micron)^3 sub-volumes. If placed together in a grid, they comprise a 50x50x12.5 micron electrode (note that the interfaces between sub-volumes will be sharp; this can be mitigated via simulating annealing/relaxation). The sub-volumes can also be used individually for a reasonably sized 12.5 micron cubic region-of-interest. A user can simply consider the voxel size to be a different value to rescale the volumes (and all of their morphological features, including particle size) as desired. These microstructures were generated using DREAM3D. The general procedure is outlined in https://doi.org/10.1016/j.jpowsour.2018.03.025 The file names are an alphabetic code having to do with the input parameters used in DREAM3D when they were generated. Most users would be best served by starting with the file property_library.html or property_library_subvols.html (which lists properties for each individual subvolume). These files contain a catalogue of the actual, measured properties of every microstructure in the database. Any combination of property values can be filtered and sorted until a desired electrode is found, at which point the user can find the file corresponding to that alphabetic code. The properties in the catalogue include connected TPB density, and for each phase: phase fraction, average particle size, polydispersity of particle size, tortuosity, and connected pair-wise interfacial area. They also include what fraction of each property is connected through to the interfaces of the volume. If the desired combination of properties is not found at first, remember that the phase IDs can be re-assigned arbitrarily, e.g. swapping 1s and 2s. In fact, the database was generated with this in mind so as not to generate redundant microstructures. If the database does not contain the desired property combinations, try to search for the other possible permutations of those properties with re-assigned phase IDs. Please cite https://doi.org/10.1149/10301.0909ecst for use. Please contact the maintainer, William K. Epting, for additional information or assistance.

3D microstructure↗
DOE OSTI2022

MOOSE Reactor Module Meshing Enhancements to Support Reactor Physics Analysis

The U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program develops an integrated suite of advanced reactor physics tools built upon the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Each code generally requires an input finite element mesh on which the physics solution is calculated, reported, and transferred to other physics codes. The meshing process is often burdensome for the complex geometries present in reactors due to lack of easy-to-use, open-source meshing tools. To address the bottleneck associated with meshing complex geometries found in nuclear reactors, NEAMS initiated the development of the MOOSE Reactor Module starting in FY21. The Reactor Module builds off the existing MOOSE Mesh System to include targeted meshing capabilities such as the ability to generate hexagonal pin cells, assemblies with ducts, rotating control drums, cores, peripheral zones around a core, as well as the automatic labeling (“reporting IDs”) of pin, assembly, and planar zones to simplify post-processing of results. As a Physics Module in MOOSE, the Reactor Module is open-source, available with any MOOSE installation, directly compatible with MOOSE-based tools, and can be invoked from MOOSEbased applications to generate meshes. Functionality from the Reactor Module has been applied to several advanced reactor concepts to demonstrate user workflow improvements and accuracy. The primary objective of the Reactor Module is to improve useability of MOOSEbased tools by streamlining mesh generation and output inspection processes. During FY22, the functionality of the Reactor Module (and accompanying Mesh System) has been expanded based on user needs. First, the Reactor Geometry Mesh Builder capability developed primarily in FY21 has been refactored and merged to the public MOOSE repository. This capability wraps underlying Reactor Module mesh generators into a “Pin – Assembly – Core” workflow appropriate for conventional Cartesian and hexagonal geometries, and notably assigns material IDs during mesh generation stage and generates only the minimal number of blocks needed in order to reduce computational burden. Biasing and boundary layer options have been added to the base mesh generators as required by thermal hydraulics solvers. The reporting ID functionality has been expanded to differentiate ring-wise and azimuthal sectors within a pin for use with depletion algorithms, and VectorPostProcessor and Reporter objects are now available to integrate solution variables across zones based on ID combinations. Functionality to trim hexagonal meshes along the center or periphery has been developed so users may leverage symmetry and reflective boundary conditions to reduce the mesh size. A flexible and powerful tool to fill the space between two sidesets has been introduced to the framework and can be used for transition layers such as stitching two assemblies together with different numbers of pins, or for complex geometries which do not follow conventional Cartesian/hexagonal patterns. Finally, additional verification problems were performed with NEAMS physics tools in complement with existing NEAMS work.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Probing Fundamental Mechanisms of Plastic Deformation with High Energy X-rays (Final Progress Report)

Overall, substantial progress was made on developing BCDI techniques in the direction of imaging defects in multi-crystal samples. Section 2.1 describes a new method to reconstruct the 3D atomic displacement field within a grain from multiple coherently resolved diffraction peaks. The key development is the ability to recover a best fit to the grain shape at the same time as the internal displacement (strain) variation with no prior knowledge of the microstructure. This is complemented by the development of a method, Sec. 2.2, to separate out sets of Laue reflections that belong to a single grain (and thus be able to index its orientation) in the commonly encountered situation in which the beam illuminates several grains in a given location. This is essential for enabling collection of coherent diffraction data (at APS 34-ID-C) from neighboring grains. All experiments depend on the quality of samples used. Although good progress has been made with thin film samples, we are working with a broader range of geometries. A new sample design uses a high-Z metal (e.g., Pt) deposited in a sub-micron trench in a low-Z metal (e.g., Al) so that the grain size is prevented from exceeding the current limit for BCDI. We also report in section 3.1 on successfully applying NF-HEDM to a fine-grain Mg sample that would normally be considered out of scope for the method. This represents progress towards being able to map out polycrystalline samples in 3D before performing BCDI on individual grains. We further report in section 3.2 on preliminary NF-HEDM characterization of a sample of UO2 that has sub-micron grains; the data imply the capability of mapping fine-grain polycrystals at 1-ID-E, again for the purposes of BCDI in the bulk. Lastly, our plan is to load grains in polycrystalline samples by inducing thermal stress. We also report the experimental measurements of the Ti- 6Al-4V alloy obtained at the European Synchrotron Radiation Facility (ESRF), which had been indexed and mapped via HEDM at APS in section 9. As discussed below, however, technique development pre-occupied the team throughout the project which meant that we did not make any significant progress towards answering the original hypotheses as stated in the proposal. In summary, we assert that we have made good progress in methods and samples and future work taking advantage of this progress is likely to obtain and analyze BCDI data from multiple adjacent grains in 3D polycrystals. Obtaining such data depends on re-gaining access to the Advanced Photon Source, which is anticipated in late summer or the Fall. In all cases, work has involved close collaboration with faculty, staff, postdocs and students from Brigham Young University (BYU), the Los Alamos National Laboratory and APS beamlines 1-ID and 34-ID.

36 MATERIALS SCIENCE↗
DOE OSTI2025

HydroFish: freshwater fish co-occurrence with hydropower plants and non-powered dams in conterminous United States sub-basins

The HydroFish dataset lists all existing hydropower plants (EHAs) and non-powered dams (NPDs; ≥ 0.001 MW potential nominal capacity), delineates the hydrologic sub-basins in which they are situated, and then lists all freshwater fish species reported to occur in those sub-basins. This dataset was compiled using the HydroBio dataset (https://hydrosource.ornl.gov/data/datasets/hydrobio/) and contains 24 total variables that describe hydrologic sub-basins, each unique EHA (plant ID and name, geographic coordinates, permit type, capacity, etc.) and NPD (ID value, known names, geographic coordinates, and estimated potential nominal capacity), and freshwater fish species in the sub-basin (common and scientific name, origin, and migratory and threat status). The HydroBio dataset was built using Oak Ridge National Laboratory’s Existing Hydropower Assets Dataset (2024 version) and Non-powered Dam Technical Potential Dataset (2024 version), and NatureServe’s fish species distribution dataset (2023 version). The dataset also contains summary variables that report the unique number of EHAs, NPDs, and freshwater fish species per sub-basin. The HydroFish dataset contains two unique data files: 1) a .csv metadata file describing the dataset variables, and 2) a .csv data file containing the actual dataset. Note that there may be many rows per unique existing hydropower plant or non-powered dam given that distinct species are listed per existing plant or NPD per sub-basin. The dataset is downloadable as a zip file containing the metadata and dataset files.

Bozeman, Bryan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Vehicular Re-Identification from Uncontrolled Multiple Views

Vehicle re-identification (re-ID) across disparate sensing modalities remains a fundamental challenge for transportation research. In this work, we introduce a deep multi-view vehicle re-ID framework that leverages Siamese networks to compare pairs of vehicle images and produce matching scores, enabling robust association across drastically different viewpoints such as those from UAVs, surveillance cameras, and ground sensors. The model exploits convolutional neural networks to learn features that remain discriminative under changes in angle, distance, and illumination, supporting more generalizable re-ID performance. As part of this effort, we also developed an automated pipeline to synchronize roadside and UAV video streams, producing a multi-perspective dataset that complements preexisting real collections and a synthetic dataset generated in this study. Together, these contributions advance the capability to re-identify vehicles across wide viewing baselines; establish a foundation for scalable, reproducible research in vehicle re-ID; and open pathways for future applications, such as inferring routine behaviors, movement patterns, and daily habits of the individual associated with the vehicle.

convolutional neural networks↗
DOE OSTI2023

Data for Hadjimichael et al. - Multi-actor, multi-impact scenario discovery

Naming convention for each file is S[scenario_id]_[realization_id].parquet All realizations are generated as per the accompanying paper: Hadjimichael, A., Reed, P.M., Quinn, J.D, Vernon, C.R., Thurber, T., Multi-actor, multi-impact scenario discovery of consequential narrative storylines for human-natural systems planning. Earth's Future (In Review) Each .parquet file contains the following columns: 'structure_id' : StateMod structure ID data corresponds to 'year': simulation year 'month': simulation month 'demand': water demand in acre-feet 'shortage': water shortage in acre-feet 'river_outflow': streamflow in diversion location in acre-feet 'control_location': other structure or reason for shortage

Hadjimichael, Antonia↗
DOE OSTI2020

HarDWR - Cumulative Water Rights Curves

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on Harmonized Water Rights Records v2.0 sourced from WestDAAT - Added "Unspecified" was a water source category, and files associated with this category v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description This product an updated version of the database used as input to the WBM model (Grogan et al. in review; Grogan et al. 2022), and is the result of the step creating cumulative water rights curves described in Lisk et al. (2024). This database contains 2,667 individual .csv files, three for each Water Management Area (WMA) in the 11-state region. File Naming WMA_[###]_[X]W.csv, where [###] is the unique identifier for each WMA, and [X] is either "S" for surface water rights, "G" for groundwater rights, or "U" for unspecified. Column headers in each file: Year: the priority date year CUML: the total cumulative water rights allocated up to this priority date year (ft3s-1) Irrigation: The percent of cumulative water rights allocated to the Irrigation category up to this priority date year (%) Domestic: The percent of cumulative water rights allocated to the Domestic category up to this priority date year (%) Livestock: The percent of cumulative water rights allocated to the Livestock category up to this priority date year (%) Fish: The percent of cumulative water rights allocated to the Fish category up to this priority date year (%) Industrial: The percent of cumulative water rights allocated to the Industrial category up to this priority date year (%) Environmental: The percent of cumulative water rights allocated to the Environmental category up to this priority date year (%) Other: The percent of cumulative water rights allocated to the Other category up to this priority date year (%) In addition to the database files, there is a companion .csv file, called stateWMAs_ID.csv. The main purpose of this file is to provide the means of translating between the [###] unique identifier and the various other id of the WMA the file is attached to. This translation file has five columns, which are: basinNum: The official state given alpha-numeric identifier of the WMA basinName: the state provided English name of the area, where applicable state: the state name uniID: a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID ID: a unique numeric identifier we created as a requirement for the files to be used within WBM (Grogan et al. in review) For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in the study region. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the second index in this object, the data for California will also be in the second index for each accompanying list. wmasRightsPersGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for groundwater. wmasRightsPersSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for surface water. wmasRightsPersUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The data frames in this object are the same as the above described .csv files, specifically for water from an unspecified source. wmasRightsTotsGrd: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for groundwater. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsSur: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for surface water. This object is provided for convenience to check out original total values, if desired. wmasRightsTotsUnsp: A list of nested lists of data frames. Each state has one entry in the top level list and consist of a list with one data frame per WMA. The values in the data frames of this object would be the equivalent to multiplying the CUML column by the sector percentage columns to create the sector total ft3s-1, specifically for water from an unspecified source. This object is provided for convenience to check out original total values, if desired. wmaIDByState: A list of data frames which contain the by state allocation by WMA matrix. There is a matrix for each state in a different data frame within the object.

Economics↗
DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗
NASA NTRS1990

The intensity dependent spread model and color constancy

Odetics is investigating the use of the intensity dependent spread (IDS) model for determining color constancy. Object segmentation is performed effortlessly by the human visual systems, but creating computer vision that takes an image as input and performs object identification on the basis of color has some difficulties. The unknown aspects of the light illuminating a scene in space or anywhere can seriously interfere with the use of color for object identification. The color of an image depends not only on the physical characteristics of the object, but also on the wavelength composition of the incident illumination. IDS processing provides the extraction of edges and of reflectance changes across edges, independent of variations in scene illumination. IDS depends solely on the ratio of the reflectances on the two sides of the edge. Researchers are in the process of using IDS to recover the reflectance image.

Kurrasch, Ellie↗
NASA NTRS1990

Intensity dependent spread processor and workstation

The Intensity Dependent Spread (IDS) is an adaptive algorithm which is modified according to the local intensity in the scene. (This results in a nonlinear process which cannot take advantage of rather nice linear transform methods.) The computation is similar to a neural net whereby intensity information is moving from each input pixel to a set of surrounding output pixels in a manner described by Cornsweet and Yellott. A prototype of a very large scale integration IDS processor is being developed and implemented in a workstation environment. The workstation consists of a SUN 3/260 and a DATACUBE pipeline processor. The IDS prototype is a board set which operates in the DATA CUBE processor. The SUN 3/260 performs control, background processing, IDS simulation and image display functions.

Westrom, George↗
NASA NTRS1992

Enhancing images with Intensity-Dependent Spread functions

The theory of Intensity-Dependent Spread functions (IDS), a model of the human visual system proposed by Cornsweet (1985), is applied to image enhancement. An artificial image is examined which illustrates the characteristics of IDS processing and shows how the theoretical results translate into visual effects. Examples of realistic scenes that have been enhanced by IDS are presented. The system is shown to be particularly useful for bringing out detail in regions of low-contrast images. IDS can be readily implemented on a parallel computer.

Reese, Greg↗
NASA NTRS1988

Computational requirements for on-orbit identification of space systems

For the future space systems, on-orbit identification (ID) capability will be required to complement on-orbit control, due to the fact that the dynamics of large space structures, spacecrafts, and antennas will not be known sufficiently from ground modeling and testing. The computational requirements for ID of flexible structures such as the space station (SS) or the large deployable reflectors (LDR) are however, extensive due to the large number of modes, sensors, and actuators. For these systems the ID algorithm operations need not be computed in real-time, only in near real-time, or an appropriate mission time. Consequently the space systems will need advanced processors and efficient parallel processing algorithm design and architectures to implement the identification algorithms in near real-time. The MAX computer currently being developed may handle such computational requirements. The purpose is to specify the on-board computational requirements for dynamic and static identification for large space structures. The computational requirements for six ID algorithms are presented in the context of three examples: the JPL/AFAL ground antenna facility, the space station (SS), and the large deployable reflector (LDR).

Hadaegh, Fred Y.↗
NASA NTRS1995

Differenced Range Versus Integrated Doppler (DRVID) ionospheric analysis of metric tracking in the Tracking and Data Relay Satellite System (TDRSS)

The Differenced Range (DR) Versus Integrated Doppler (ID) (DRVID) method exploits the opposition of high-frequency signal versus phase retardation by plasma media to obtain information about the plasma's corruption of simultaneous range and Doppler spacecraft tracking measurements. Thus, DR Plus ID (DRPID) is an observable independent of plasma refraction, while actual DRVID (DR minus ID) measures the time variation of the path electron content independently of spacecraft motion. The DRVID principle has been known since 1961. It has been used to observe interplanetary plasmas, is implemented in Deep Space Network tracking hardware, and has recently been applied to single-frequency Global Positioning System user navigation This paper discusses exploration at the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) of DRVID synthesized from simultaneous two-way range and Doppler tracking for low Earth-orbiting missions supported by the Tracking and Data Relay Satellite System (TDRSS) The paper presents comparisons of actual DR and ID residuals and relates those comparisons to predictions of the Bent model. The complications due to the pilot tone influence on relayed Doppler measurements are considered. Further use of DRVID to evaluate ionospheric models is discussed, as is use of DRPID in reducing dependence on ionospheric modeling in orbit determination.

Radomski, M. S.↗
NASA NTRS2001

Wind Advisory System

The present invention is a two-part wind advisory system comprising a ground station at an airfield and an airborne unit placed inside an aircraft. The ground station monitors wind conditions (wind speed, wind direction, and wind gust) at the airfield and transmits the wind conditions and an airfield ID to the airborne unit. The airborne unit identifies the airfield by comparing the received airfield ID with airfield IDs stored in a database. The airborne unit also calculates the headwind and crosswind for each runway in both directions at the airfield using the received wind conditions and runway information stored in the database. The airborne unit then determines a recommended runway for takeoff and landing operations of the aircraft based on th~ runway having the greatest headwind value and displays the airfield ID, wind conditions, and recommended runway to the pilot. Another embodiment of the present invention includes a wireless internet based airborne unit in which the airborne unit can receive the wind conditions from the ground station over the internet.

Curto, Paul A.↗